#!/usr/bin/env python
# encoding: utf-8

# The MIT License (MIT)

# Copyright © 2019 Jan Freyberg

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.


import torch.nn as nn
from torch.autograd import Function

class RevGrad(Function):
    @staticmethod
    def forward(ctx, input_):
        ctx.save_for_backward(input_)
        output = input_
        return output

    @staticmethod
    def backward(ctx, grad_output):  # pragma: no cover
        grad_input = None
        if ctx.needs_input_grad[0]:
            grad_input = -grad_output
        return grad_input

revgrad = RevGrad.apply


class GradientReversal(nn.Module):
    def __init__(self, *args, **kwargs):
        """
        A gradient reversal layer.
        This layer has no parameters, and simply reverses the gradient
        in the backward pass.
        """

        super().__init__(*args, **kwargs)

    def forward(self, input_):
        return revgrad(input_)
